The numbers don't lie. According to recent studies,
Python is the most loving programming language for data scientists. You need a
user-friendly language that provides adequate library availability and
excellent community participation. Projects with inactive communities are
generally less likely to have their platforms serviced or updated, which is not
the case with Python.
What makes Python great for data science? We
explored why Python is so common in the booming data science industry - and how
you can use it in your big data and machine
learning projects.
Why Python is best for Data Science?
Most of the programmers used python for the data
science because python is easy to use and its syntax is very easy to
understand.
Python has long been known as a programming
language that is syntactically easy to understand anyway. Python also has an
active community with a huge selection of libraries and resources. The result?
They have a programming platform that makes sense for new technologies such as
machine learning and data science.
Professionals who work with data science
applications do not want to get stuck in complex programming requirements. You
want to use programming languages like Python and Ruby to perform easy tasks.
Ruby is great for tasks like cleaning and
merging data, as well as other data preprocessing tasks. However, there aren't
as many machine learning libraries as Python. This gives Python the edge in data
science and machine learning
With Python, developers can also deploy
programs and run prototypes, which speeds up the development process. Once a
project becomes an analysis tool or application, it can be ported to more
complex languages such as Java or C if necessary.
New data scientists are attracted to Python
because of its ease of use, which makes it accessible. So popular, in fact,
that 48% of data scientists with five years or less experience-rated Python as
their preferred programming language.
This number gradually decreases with increasing
level of experience and the analyses become more intensive. Python has proven
to be a great place to start for data scientists
Why Data Science and Python Good to use Together
In data science, useful information is
extrapolated from huge pools of statistics, registers, and data. These data are
generally unsorted and difficult to correlate with significant accuracy. Machine
learning can link different data sets but requires serious sophistication and
computing power.
Python fulfills this need by being a universal
programming language. You can use it to create a CSV output for easy reading of
data in a table. Alternatively, more complicated file output that machine
learning clusters can include for computation.
Consider the following example:
The weather forecast builds on previous records
from a century of weather data. Machine learning can create more accurate
forecast models based on past weather events. Python can do this because it is
easy and efficient for code execution, but also multifunctional. In addition,
Python can support object-oriented, structured and functional programming
styles so that it can be used anywhere.
The Python package index now contains over
70,000 libraries and that number continue to grow. As already mentioned,
Python offers many libraries that are geared toward data science. A simple
Google search shows many Python top 10 libraries for data science lists. We
could say that the most popular data analysis library is an open-source library
called Pandas. It is a collection of high-performance applications that make
analyzing data in Python a much easier task.
Regardless of what scientists want to do with
Python, be it predictive causal analysis or prescriptive analysis, Python has
the toolbox to perform a variety of powerful functions. No wonder data
scientists have adopted Python.
Conclusion:
I hope now you have understood why most data
scientists are using python. NearLearn provides the best data science with
python training in Bangalore. it also provides Artificial Intelligence, Machine
Near Learn provides Best Data
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Read More: Top 5 Data
science Trends in 2020
